Data Matters

“It is a capital mistake to theorize before one has data.” - Sherlock Holmes in “A study in Scarlet” by Sir Arthur Conan Doyle

In the summer of 2020, OpenAI unveiled a groundbreaking model named GPT-3. This model, characterized by its human-like interactions, marked a significant leap in the realm of generative AI. Subsequent models, such as ChatGPT and GPT-4, further solidified the prowess and applicability of these AI tools. This evolution sparked a global race to harness the potential of generative AI for diverse applications, from answering intricate questions to offering legal counsel and even solving mathematical problems.

Central to this AI revolution is the pivotal role of data. The transformation from the days when data collection and processing were colossal challenges to today's era, where data models encompass billions or even trillions of elements, is nothing short of remarkable. With the advent of cloud computing, big data, and advanced machine learning techniques, data volumes have escalated from mere gigabytes to petabytes and beyond.

Despit this progress, a paradox exists. Many businesses grapple with harnessing data to its full potential and making it usable enough for actionable insights. The onus often falls on IT departments, which, while adept at their specific roles operate in isolation, potentially missing the broader business perspective.

This article aims to offer business users a simplified understanding of the data use decision process. It can be seen as a a roadmap for business leaders keen on collaborating with their IT counterparts to unlock the true potential of their data.

The Four-Step Framework to Data Usability

1. Data Identification and Collection

The first step is foundational. Every piece of data that hasn't been identified or collected essentially remains dormant, offering no value. While it's crucial to remain vigilant about potential data sources that might be integrated in the future, the immediate focus should be on the data that has been identified and available for collection. Business leaders and IT teams must collaboratively identify all available data sources and work on collecting everything possible.

2. Quality Assurance of Collected Data

Having data is one thing; ensuring its quality is another. Not all collected data is immediately usable. There might be anomalies, like implausible phone numbers or inconsistent date formats. Furthermore, the data might suffer from False Positive errors (incorrectly meeting criteria) or False Negative errors (overlooking valid records). Rigorous quality checks are imperative to ensure that the data is not just abundant but also accurate and reliable. IT departments, with their technical expertise, play a pivotal role here, but they need clear business criteria to ensure the checks are relevant.

3. Data Usability Assessment

Once the data's quality is ascertained, the next step is to categorize it based on its immediate usability. Data that can drive immediate business decisions or strategies should be prioritized. Conversely, data that might be relevant in the future, given market trends or anticipated business shifts, should be archived efficiently. This step requires a harmonious blend of IT's technical insights and the strategic foresight of business leaders.

4. Action Plan for Redundant Data

In the dynamic world of business, not all data retains its relevance indefinitely. Over time, certain datasets might become obsolete. It's essential to have a clear strategy for such data. Some businesses might find value in sharing it with strategic partners or even monetizing it. However, once its utility is exhausted, it's crucial to purge such data, ensuring optimal resource utilization and data security.

Conclusion: The Strategic Advantage of Data Usability

The rapid advancements in AI, exemplified by models like GPT-3 and GPT-4, underscore the transformative power of data. However, this potential can only be realized when businesses and IT departments align their objectives and collaborate seamlessly.

The proposed four-step framework simplifies the intricate process of data usability. By adopting this approach, businesses can not only make informed decisions but also expedite their go-to-market strategies, gaining a competitive edge in today's data-driven landscape.

While this article offers a streamlined approach, it's essential to remember that data usability is a continuous journey. Regular reviews, updates, and collaborations between business and IT leaders are crucial to stay ahead in the ever-evolving world of data and technology.